This invention relates generally to computer simulation and more particularly to a method of modeling a factory including human operators and of validating such a model.
Computer models have been used to study factories for the purpose of analyzing their capacities and behaviors such as product throughput, product cycle times, and work-in-process inventory levels. In distinction to queuing theory, discrete event simulation is a preferred approach for dealing with complex factories that involve many products and machines, machine breakdowns, resource limitations and dispatching rules more complex than a first-in, first-out ("FIFO") approach.
These computer models are also used to represent real factories and to run scenarios of where products may be in such a factory at some future time. Some of the models are created with modeling languages having the capability to describe requirements for resources needed to carry out and complete individual activities. But these languages typically have not provided a capability of naturally describing situations in which individual entities having a variety of resources can be captured according to complex algorithms involving the suitability of the entities to meet the resource requirements and at the same time maximize the utilization of scarce resources. In particular, existing models have not been able to account for various human resources that are needed to operate and maintain the equipment in a semiconductor fabrication facility ("fab").
When a discrete event simulation model has been created, the model must be verified and validated. Verification means assuring that there are no inaccurate representations in the elements and structure of the model. Validation means demonstrating that the responses of the model accurately duplicate the real factory and vice versa. Verification and validation have been accomplished by showing that behaviors such as throughput, product cycle times and work-in-process inventory levels have the same steady-state averages and variances in data from the model as they have in data from the real factory.
Discrete event simulations track the sequence of state changes in the modeled factory as a function of time; therefore such simulations inherently generate data that can be used to trace the progress of individual products over time as they progress through the factory. This is demonstrated in some simulations by graphically displaying a representation of the factory that shows objects moving from machine to machine during the course of the simulation horizon. A shortcoming of this graphical approach to inspecting a simulation performance is that slowing the simulation down for human interpretation makes the simulation time unacceptably long for routine analyses.
Verification and validation is a critical aspect of computer factory simulation. Previous work has focused such efforts on inspecting statistics of overall measures like mean and variance of throughput, cycle time, and work-in-process (WIP). Some more recent work, such as that being done at SEMATECH, places a new emphasis on graphical animations that allow the modeler to confirm the accuracy of the model (verification) but that are not well suited to validate a model as complex as required for wafer movement through a semiconductor fabrication facility where the purpose is to focus on throughput, cycle time and WIP. Statistical parameters summarize behaviors down to a few numerical outcomes and, in the process, hide many of the causes of the behavior of the model.
From the foregoing it will be apparent that there is a need for a way to simulate the humans as well as the machines in a factory and to verify and validate such a factory simulation model.